Intra-day coordination control method for participation of fused magnesium group furnaces in primary frequency modulation

Abstract

The invention provides an intra-day coordination control method for participation of fused magnesium group furnaces in primary frequency modulation, and belongs to the field of primary frequency modulation of power systems. The method comprises the following steps: 1) selecting a centralized control framework as a control framework for real-time coordinated regulation and control of the electric smelting magnesium group furnace; and 2) designing a control strategy to coordinate the adjustment power of each electric smelting furnace for magnesium. The timeliness of control is fully considered, and the state change of the electric smelting furnace in a future period of time is predicted based on the production state of the electric smelting furnace in a past period of time by using a time sequence prediction method; and 3) providing a control strategy for dynamic grouping of the fused magnesium group furnaces, constructing an economic optimization regulation and control model considering the adjustment cost, solving the optimal primary frequency modulation power of each fused magnesium group furnace at the current moment, and continuously rolling along with adjustment to optimize the capacity of the fused magnesium group furnaces participating in the primary frequency modulation. According to the adjustment control mode considering the economic cost, the adjustment cost can be reduced on the premise that the loss of the primary frequency modulation power is extremely low, and the overall economical efficiency of the fused magnesium enterprise participating in the primary frequency modulation is improved.

Description

Technical Field

[0001] The present invention belongs to the field of primary frequency regulation of electric power system, and relates to a day-time coordinated control method for a group of fused magnesium furnaces participating in primary frequency regulation, and applies the method to the participation of high-energy-consuming fused magnesium furnace loads in primary frequency regulation of power grid, so that the group of fused magnesium furnaces are capable of performing primary frequency regulation following the frequency difference change of power grid. Background Art

[0002] Building a new power system with new energy as the main body is an inevitable trend of low-carbon development. As conventional units are gradually replaced by new energy, the system's ability to maintain frequency stability continues to decline. Compared with conventional units, demand-side flexible loads can improve system frequency stability through their flexibility; among them, high-energy-consuming industrial loads have large load capacity and high automation level, and are effective resources for new power systems to improve flexibility and stability. As a typical high-energy-consuming industrial load, the electric fused magnesium furnace has a fast response speed, is easy to accurately control and adjust in both directions, and has the potential to participate in the primary frequency regulation of the system. Therefore, it is necessary to study the implementation method of its participation in the primary frequency regulation of the power grid, so as to realize the participation of load-side resources in system frequency control and better maintain the safety of system operation.

[0003] As a good flexibility resource on the load side, high-energy-consuming fused magnesium furnaces have the ability to participate in the primary frequency regulation of the power grid. However, fused magnesium enterprises currently do not participate in the auxiliary services of the primary frequency regulation of the power grid, and fail to exert their primary frequency regulation capabilities to help ensure the frequency security of the power grid. If we can deeply explore the primary frequency regulation potential of fused magnesium furnaces and design reasonable operation plans and strategies, we can enable them to effectively participate in the primary frequency regulation services of the power grid, and provide an alternative supplement to make up for the lack of frequency response resources of the new energy power grid.

[0004] Under the compensation mechanism for primary frequency regulation auxiliary services of single load-side resources, the flexible resource subject reports its primary frequency regulation adjustable capacity on the operating day (D-day) as a market commodity to the system on D-1. The power dispatching agency conducts primary frequency regulation market clearing calculations, and based on the declared data of the primary frequency regulation service providers, combined with the primary frequency regulation market demand and historical primary frequency regulation performance indicators, etc., with the goal of minimizing the primary frequency regulation service cost, conducts centralized clearing of the primary frequency regulation market, and announces the winning results. The primary frequency regulation service won by the flexible resource subject is settled at its declared price. During the operating day, according to the real-time active power deviation and frequency fluctuation information of the power grid, and based on the winning capacity of the flexible resource subject in each period, the primary frequency regulation of each subject is assessed to follow the frequency fluctuation of the power grid. Each subject must respond to the frequency deviation signal of the system by adjusting its own output to avoid being punished for failing to follow the frequency difference signal. It can be seen that the winning capacity of each subject during the day becomes the target threshold benchmark for the primary frequency regulation output.

[0005] During the real-time control stage of the operation day, the dispatching agency assesses the performance of the group of fused magnesium furnaces in tracking the frequency difference signal, and conducts capacity settlement and cumulative power penalties for insufficient frequency regulation on the fused magnesium enterprises. Therefore, real-time coordinated control is crucial to the overall economic efficiency of the operation of the fused magnesium enterprises, and it is necessary to coordinate the group of fused magnesium furnaces to accurately track the frequency modulation signal. However, since the fused magnesium furnace participates in the primary frequency modulation to adjust the power, it has a certain impact on its own production and equipment life, and good frequency modulation performance does not represent the optimal overall economy. For this reason, during the intraday real-time coordinated control stage, a coordinated control method that takes into account the economic cost of the adjustment is used to coordinate the group of fused magnesium furnaces to track the frequency modulation signal. Summary of the invention

[0006] Designing a reasonable coordinated control strategy for the group of furnaces in the intraday real-time control stage is critical to ensure the overall economic efficiency of the fused magnesium enterprise. Since the operating time and specific state variation range of each furnace in the group of furnaces of the fused magnesium enterprise are large, the goal of the intraday real-time control stage is to accurately describe the working conditions of each furnace as much as possible according to the real-time demand of the primary frequency regulation of the power grid during each dispatching period, and then coordinate the output of the fused magnesium group of furnaces to respond to the primary frequency regulation demand of the power grid according to the winning capacity. If the intraday real-time control stage cannot respond to the frequency fluctuation signal in a timely and accurate manner, it will affect the primary frequency regulation income of the enterprise. However, the power regulation of the fused magnesium furnace participating in the primary frequency regulation will also have a certain impact on the normal production of the fused magnesium group of furnaces and the life of the equipment. Therefore, a good frequency regulation effect cannot represent the optimal overall economic efficiency of the operation of the fused magnesium enterprise.

[0007] In view of the above problems, the present invention first designs a control structure for the electric molten magnesium furnace group to participate in the primary frequency modulation; in order to quickly obtain the real-time optimal decision of the electric molten magnesium furnace, the control measures for the dynamic grouping of the electric molten magnesium furnace group are determined and the operating conditions of the electric molten magnesium furnace group are predicted in the short term; with the goal of minimizing the intra-day penalty, the economic optimization control model considering the regulation cost is used to coordinate the rolling optimization allocation of the primary frequency modulation power of the group of furnaces.

[0008] The intraday stage needs to take the previous day's winning capacity as the benchmark. This capacity corresponds to the adjustment power of the fused magnesium furnace group when the grid frequency difference is 0.2Hz. Then, the frequency modulation coefficient K of the fused magnesium furnace group in each period can be obtained. t The mismatch between supply and demand during system operation will cause grid frequency fluctuations. When the grid frequency exceeds the dead zone, the fused magnesium furnaces on the load side need to quickly participate in grid frequency regulation based on the real-time frequency deviation Δf and the frequency regulation coefficient K in the current period. t To adjust its own smelting power to participate in the primary frequency regulation of the power grid. t (Δf-Δf db) adjustment amount, the dispatching agency will punish the fused magnesium furnace group according to the deviation between the actual primary frequency regulation power and the primary frequency regulation demand at the current moment. Since the frequency fluctuation of the power system is random and the adjustment capacity of the fused magnesium furnace group is also changing all the time, different group coordination control strategies will lead to significant differences in the economic cost of primary frequency regulation, which will in turn affect the overall economic efficiency of the fused magnesium furnace group participating in the primary frequency regulation of the power grid. Therefore, it is crucial to design an appropriate intraday coordination control strategy for the fused magnesium furnace group participating in the primary frequency regulation of the power grid during the intraday control stage to improve the economic benefits of the fused magnesium furnace group participating in the primary frequency regulation.

[0009] Compared with the high randomness of the frequency change of the power grid, the dynamic change process of the working condition of the fused magnesium furnace can be predicted in the short term. The normal production state of the fused magnesium furnace is cycled under the three working conditions of "feeding → main melting → exhaust". Although the duration of various working conditions is not completely fixed, the working state of the fused magnesium furnace group can be predicted in the short term based on the production status over the past period of time, thereby clarifying its adjustment capacity. Therefore, the present invention matches the real-time collected power grid frequency signal by predicting and controlling the future state of the fused magnesium furnace group. According to the change of the power grid frequency, combined with the frequency modulation coefficient K of the current adjustment period t , considering the economic cost of adjustment, dynamic decision-making is made on the real-time adjustment power of the fused magnesium furnace group to achieve the optimal overall economy.

[0010] In order to achieve the above object, the technical solution adopted by the present invention is:

[0011] A method for intraday coordinated control of a group of fused magnesium furnaces participating in primary frequency modulation, comprising the following steps: 1) determining a control architecture for the group of fused magnesium furnaces participating in primary frequency modulation. The present invention selects a centralized control architecture as a control architecture for real-time coordinated control of a group of fused magnesium furnaces; 2) formulating an appropriate control strategy to coordinate the power adjustment of each fused magnesium furnace. Taking full account of the timeliness of the control, the present invention uses a time series prediction method to predict the state change of the fused magnesium furnace in the future based on its production state in the past; 3) proposing a control strategy for dynamic grouping of fused magnesium furnaces. Based on the working condition prediction of the fused magnesium furnace and combined with the real-time collected power grid frequency information, an economic optimization control model considering the regulation cost is proposed, and the optimal primary frequency modulation power of each fused magnesium furnace at the current moment is solved under the condition of satisfying relevant constraints and control objectives, and the capacity of the group of fused magnesium furnaces participating in primary frequency modulation is continuously optimized as the regulation proceeds. Specifically, the following steps are included:

[0012] Step 1: Design the control structure for the fused magnesium furnace group to participate in the primary frequency modulation. During the intraday real-time adjustment stage, the production control center needs to quickly control the power changes of the fused magnesium furnace group so that the fused magnesium furnace group can perform primary frequency modulation following the frequency difference changes of the power grid.

[0013] Taking into account the operability, timeliness and controllability of the control architecture, combined with the characteristics of the electric molten magnesium furnace group, which is small in scale, has a large smelting power of a single furnace, and can only adjust the smelting power under the main smelting condition, the present invention proposes a centralized control architecture, which directly exchanges information between each electric molten magnesium furnace and the control center through the production communication network. The production control center gathers and integrates smelting data, comprehensively perceives the status of the group of furnaces, and optimizes the decision-making and coordinated control of the group of furnaces from an overall perspective. The centralized control architecture is convenient for coordinating the adjustment of the smelting power of each electric molten magnesium furnace because it can integrate all the characteristic information of each electric molten magnesium furnace.

[0014] Under the centralized control architecture, the control process of the fused magnesium furnace group participating in the primary frequency modulation is divided into different adjustment periods according to whether the grid frequency crosses the dead zone: when the grid frequency does not exceed the dead zone (frequency is stable), the fused magnesium furnace group produces normally and does not adjust the operating power; after the grid frequency crosses the upper limit of the dead zone, the fused magnesium furnace group increases the operating power and uses more electricity to reduce the grid frequency; when the grid frequency exceeds the dead zone (crossing the upper limit of the dead zone is called frequency upward deviation, and crossing the lower limit of the dead zone is called frequency downward deviation), the fused magnesium furnace group should be based on the frequency modulation coefficient K of the fused magnesium furnace group in the previous period t , linearly responds to the real-time frequency deviation of the power grid to provide primary frequency regulation capacity P t PFR , until the grid frequency returns to the dead zone range. The centralized control architecture is divided into the control layer and the equipment layer according to the coverage of the energy flow and information flow of the control project. The details are as follows:

[0015] The control layer is used to determine the winning capacity P of the fused magnesium furnace group in the current period. PFR , the capacity corresponds to the grid frequency deviation exceeding the frequency deviation allowable value Δf th The primary frequency modulation capacity that the fused magnesium furnace group should provide at this time is the frequency modulation coefficient K of the current period. t :

[0016]

[0017] Where Δf db Indicates the primary frequency modulation dead zone;

[0018] The production control center collects the grid frequency change information Δf in real time. If the grid frequency crosses the primary frequency regulation dead zone at this time, the production control center calculates the primary frequency regulation demand P of the current grid for the fused magnesium furnace group based on the power-frequency characteristics of the fused magnesium furnace. t PFR :

[0019]

[0020] Where Δf t Indicates the real-time frequency deviation of the power grid; Indicates that the grid frequency deviation exceeds the frequency regulation dead zone but is not higher than the allowable value of frequency deviation; Indicates that the grid frequency deviation is higher than the allowable frequency deviation value.

[0021] At the same time, the adjustable capacity ΔP of the fused magnesium furnace at the current moment is evaluated through equipment layer perception t ad , combined with the grid primary frequency regulation demand P t PFR , real-time rolling optimization of the actual primary frequency modulation power ΔP of the fused magnesium furnace t act :

[0022]

[0023] In the formula, It represents the actual primary frequency modulation power of the jth fused magnesium furnace in the fused magnesium furnace group at time t, which is the decision variable for intraday economic optimization and regulation and is defined as the adjustable power; n is the number of fused magnesium furnaces involved in the adjustment.

[0024] The group furnace controller monitors the smelting status of each fused magnesium furnace and the adjustable power in real time Based on this, and combined with the adjustment status of each electric magnesium furnace in the previous period, the controller will optimize the primary frequency modulation power of each electric magnesium furnace and send it down. The electric magnesium furnace is connected to the public power grid through the Point of Common Coupling (PCC) to help the system suppress the grid frequency fluctuation caused by the imbalance between supply and demand power.

[0025] Step 2: Predict the operating conditions of the molten magnesium furnace. The molten magnesium furnace is a typical strongly coupled, time-varying, and strongly nonlinear system. Its full parameter description cannot be obtained by mathematical analysis. The present invention uses a time series prediction method to predict the state changes of the molten magnesium furnace in the future based on the production state of the molten magnesium furnace in the past period of time, providing a basis for rolling optimization decisions. The AutoRegressive-Moving-Average (ARMA) model is used to predict the adjustment capacity of the molten magnesium furnace group over a period of time. First, the input data needs to be tested for data stationarity using the Augmented Dickey-Fuller (ADF) test, and non-stationary data needs to be differentially processed; then, the tailing characteristics of the autocorrelation function (ACF) graph in the ADF test result and the truncation characteristics of the partial autocorrelation function (PACF) graph are combined with the evaluation criteria for model identification and order determination. The specific evaluation criteria are shown in Table 1.

[0026] Table 1 Evaluation criteria

[0027]

[0028] In Table 1: ACF represents autocorrelation function; PACF represents partial autocorrelation function; AR(p) represents p-order autoregressive model; MA(q) represents q-order moving average model; ARMA(p,q) represents p-order autoregressive and q-order moving average model; “truncation after order p” means that when the order of the autoregressive process is p, the autocorrelation function value with an interval greater than p is zero; “truncation after order q” means that when the order of the moving average process is q, the autocorrelation function value with an interval greater than q is zero; “tailing” means that the autocovariance function and autocorrelation function of the autoregressive or moving average process have the same form of difference equation.

[0029] Determine the specific prediction model, which is described as:

[0030]

[0031] In the formula, Ψ t represents the predicted regulation capacity of the fused magnesium furnace; p and q represent the autoregressive order of the prediction model, where α a , β a They represent the undetermined coefficients that are not “0”; t , χ t-b represents an independent error term; X t-a represents a stationary, normal time series.

[0032] Finally, the infer() function in MATLAB is used to calculate the difference between the ARMA model predicted data and the actual data, that is, the residual test.

[0033] Since the fused magnesium furnace can only adjust the smelting conditions under the main melting condition, in order to reduce the decision variables of the real-time coordinated control optimization solution, a control strategy for dynamic grouping of fused magnesium furnaces is proposed. According to the different operating states of the fused magnesium furnace, it is divided into the main melting cluster, the feeding cluster and the exhaust cluster, and dynamically adjusted according to the smelting process. The ARMA model can be used to predict the working state of each fused magnesium furnace at a certain time, and the fused magnesium furnaces (fused magnesium furnaces in the main melting condition) that can participate in the adjustment in each control period are selected according to the prediction results to participate in the subsequent primary frequency modulation power optimization.

[0034] Step 3: Rolling optimization of the primary frequency modulation power of the fused magnesium furnace group. Intraday coordinated control requires real-time control, and does not require direct global optimization control, but considers optimizing decisions through the current local time period. Therefore, the present invention continuously rolls the limited optimization prediction time domain and realizes global optimization on the basis of local optimization. The optimization result is N uThe control sequence within the time period, but in actual application, only the first control result is executed, and the above process is repeated at the next sampling moment. At each sampling moment, the current state of the system is used as the initial condition for optimization. This strategy makes each rolling optimization more in line with the frequency stability requirements of the current system and the actual working status of each molten magnesium furnace, thereby improving the robustness and effectiveness of the molten magnesium furnace group participating in the primary frequency regulation of the power grid. Since this continuous rolling optimization process itself can adapt to the changes in the state of the molten magnesium furnace group, there is no need to set up a feedback correction link.

[0035] The prediction time domain is N p The control time domain consists of N time periods. u The period composition (N u <N p ). In this optimization framework, using N p The prediction results of each period are used to characterize the future behavior of the fused magnesium furnace group, and the next N u Control target for each period of time.

[0036] The key to whether the overall economic improvement and frequency regulation requirements can be taken into account during the real-time adjustment stage lies in the design of the economic cost function. Based on the idea of ​​rolling optimization, the present invention proposes an economic optimization control model that takes into account the adjustment characteristics of the group of fused magnesium furnaces. The goal is to minimize the economic cost after the group of furnaces participates in the primary frequency regulation, and consider the achievement of the primary frequency regulation target of the group of fused magnesium furnaces. The compensation income for the primary frequency regulation of fused magnesium is determined by the primary frequency regulation capacity that won the bid the day before. Therefore, the consideration of the economic efficiency within the day is mainly to minimize the economic cost. The frequency regulation deficiency penalty, production loss, power over-limit penalty and equipment loss of the group of furnaces reflect the economic cost of the real-time adjustment stage. The effect of the group of furnaces adjusting the power in real time to track the frequency difference signal reflects its primary frequency regulation performance. Therefore, the economic optimization control model is designed as follows:

[0037]

[0038] Where Y represents the economic cost of the real-time adjustment stage; Y1 represents the penalty for insufficient frequency regulation in the control time domain; Y2 represents the production loss in the control time domain; Y3 represents the penalty for power over-limit in the control time domain; Y4 represents the equipment loss in the control time domain; t represents the rolling time domain, N u represents the control time domain; J represents the number of fused magnesium furnaces in the enterprise; T represents the scheduling cycle; P j,t It represents the operating power of the jth fused magnesium furnace in the fused magnesium furnace group at time t; represents the rated power of the jth fused magnesium furnace in the fused magnesium furnace group at time t; Ω represents the maximum number of adjustments of a single furnace within 15 minutes; They represent the 0-1 variables of the power adjustment of the fused magnesium furnace in the τth adjustment stage, Indicates that the current stage is upward. Indicates an upward adjustment at the current stage; They represent the 0-1 variables of the power adjustment of the fused magnesium furnace in the τth and τ-1th adjustment stages, respectively. Indicates the current stage of adjustment. Indicates that there is no adjustment in the current stage; N(t) indicates the number of fused magnesium furnaces that can participate in the adjustment in the tth period; They represent the power increase and decrease of the jth fused magnesium furnace in the fused magnesium furnace group at time t; P min , P max Respectively represent the minimum and maximum power of the electric magnesium furnace, P min Generally P max Generally

[0039] The penalty Y1 for insufficient frequency modulation in the control time domain is:

[0040]

[0041] Where Δf represents the real-time frequency deviation; ε represents the penalty unit price for insufficient frequency modulation; X Δf＞0.033Hz Indicates that the grid frequency difference exceeds the upper limit of the frequency regulation dead zone; X Δf＜-0.033Hz It means that the grid frequency difference exceeds the lower limit of the frequency regulation dead zone.

[0042] If the primary frequency modulation power of the fused magnesium furnace exceeds the adjustable range allowed by the fused magnesium furnace at that moment, it will affect the product quality. In the real-time adjustment stage during the day, in order to participate more in the primary frequency modulation and improve the overall economy, some periods will exceed the adjustable range allowed by the fused magnesium furnace at that moment, resulting in certain production losses, that is, the production loss Y2 in the control time domain:

[0043]

[0044] In the formula, represents the indicator function, When it is true, the function value is 1, otherwise it is 0; κ represents the unit price of production loss of electric fused magnesium furnace; represents the indicator function, The function value is 1 if it is true, otherwise it is 0.

[0045] Control the power limit penalty Y3 in the time domain:

[0046]

[0047] Where P M represents the maximum demand contract value; ω represents the maximum demand electricity price;

[0048] Control the equipment loss Y4 in the time domain:

[0049]

[0050] In the formula, 3 represents the three-phase electrode; s t It represents the number of adjustments in the tth period; φ represents the unit price of loss each time the equipment is adjusted.

[0051] In the model, except for the start time, other (N u -1) The working state of the fused magnesium furnace at each decision moment needs to be based on the predicted working state value plus the frequency adjustment amount decided from the predicted moment to the moment before that moment, so that the influence of the decision result of the previous moment on the decision of the subsequent moment can be considered in the entire optimization period. According to the above model, N u Each fused magnesium furnace has a frequency modulation power control sequence during the time period, and forms N u The actual primary frequency modulation total power control sequence of the group furnaces in the time period. Under the centralized control structure, the control center sends the first control result of the primary frequency modulation power adjustment of each fused magnesium furnace to each fused magnesium furnace. Repeat the above process at the next sampling time.

[0052] In summary, the study of the intraday coordinated control of the electric fused magnesium furnace group participating in the primary frequency modulation requires the coordination of the electric fused magnesium furnace group to accurately track the frequency modulation signal. However, since the electric fused magnesium furnace participates in the primary frequency modulation to adjust the power to a certain extent, its own production and equipment life, good frequency modulation performance does not represent the optimal overall economic performance. To this end, in the intraday real-time coordinated control stage, a coordinated control method that considers the economic cost of the adjustment is used to coordinate the electric fused magnesium furnace group to track the frequency modulation signal. First, it is necessary to determine the control architecture of the electric fused magnesium furnace group participating in the primary frequency modulation, and then analyze its frequency modulation performance and formulate its optimization control strategy accordingly. The present invention selects a centralized control architecture as the basis for the real-time coordinated control of the electric fused magnesium furnace group. Based on the working condition prediction of the electric fused magnesium furnace, combined with the real-time collected power grid frequency information, the optimal primary frequency modulation power of each electric fused magnesium furnace at the current moment is solved under the condition of meeting the relevant constraints and control objectives, and the capacity of the electric fused magnesium furnace group participating in the primary frequency modulation is continuously optimized as the adjustment proceeds. Finally, an intraday coordinated control model for the electric fused magnesium furnace group participating in the primary frequency modulation considering the economic cost of the adjustment is established.

[0053] The beneficial effects of the present invention are:

[0054] (1) The present invention utilizes the ARMA prediction model to make a short-term prediction of the smelting state of each molten magnesium furnace in the future based on the current and past operating state change trends of the molten magnesium furnace, clarify the real-time group furnace adjustment capacity, and facilitate the rolling optimization model to optimize the primary frequency regulation capacity according to different frequency changes of the power grid. In order to ensure the accuracy of the prediction, it is necessary to update the status data of the molten magnesium furnace group in real time so as to make a rolling prediction of the group furnace adjustment state in the next control time domain. After the adjustment, the status of the molten magnesium furnace group is updated in real time, and the group furnace adjustment state in the next control time domain is rolled out to ensure the accuracy of the prediction. With the continuous updating of real-time data, the prediction model can more accurately reflect the actual changes in the status of the molten magnesium furnace, which is helpful to make the optimal control decision.

[0055] (2) The present invention is based on the prediction of the working conditions of the fused magnesium furnace, combined with the real-time collected power grid frequency information, and rollingly solves the real-time optimal primary frequency regulation capacity of the fused magnesium furnace group under the condition of meeting the relevant constraints and control objectives. The control method considering the regulation cost proposed in the present invention can effectively realize the intraday coordinated control of the fused magnesium furnace group participating in the primary frequency regulation of the power grid. The regulation control method considering the economic cost can reduce the regulation cost and reduce the economic losses of the enterprise under the premise of losing very little primary frequency regulation power, improve the overall economy while ensuring the frequency regulation quality of the primary frequency regulation, which is conducive to the improvement of the overall economy of the fused magnesium enterprise participating in the primary frequency regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is the schematic diagram of the centralized control architecture;

[0057] Figure 2 A centralized control structure for primary frequency modulation of a group of fused magnesium furnaces;

[0058] Figure 3 Schematic diagram for control period division;

[0059] Figure 4 Adjustment capacity prediction flow chart for fused magnesium furnace group;

[0060] Figure 5 Optimize schematics for scrolling;

[0061] Figure 6 The results of the data stationarity test; (a) is the ACF graph; (b) is the PACF graph;

[0062] Figure 7 is the standardized residual frequency distribution histogram;

[0063] Figure 8 The working condition prediction of the electric fused magnesium furnace; (a) is the working condition prediction diagram of the first electric fused magnesium furnace; (b) is the working condition prediction diagram of the second electric fused magnesium furnace; (c) is the working condition prediction diagram of the 30th electric fused magnesium furnace;

[0064] Fig. 9 (a) is the power regulation of the fused magnesium furnace group; (b) is the power regulation diagram of the fused magnesium furnace group; (c) is Fig. 9 (b) is a partial enlarged view of point A; (d) is Fig. 9 (b) A local enlarged view of point B;

[0065] Fig.10 The adjustment results considering economic cost; (a) is the frequency difference of power grid; (b) is the frequency regulation coefficient of fused magnesium furnace group;

[0066] (c) is the primary frequency regulation power of the fused magnesium furnace group; (d) is the insufficient frequency regulation power; (e) is the total regulation cost;

[0067] Fig.11 is the adjustment result without considering the economic cost; (a) is the grid frequency difference; (b) is the frequency regulation coefficient of the fused magnesium furnace group;

[0068] (c) is the primary frequency regulation power of the fused magnesium furnace group; (d) is the insufficient frequency regulation power; (e) is the total regulation cost;

[0069] Fig.12 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0070] The present invention is further described below in conjunction with specific embodiments.

[0071] The goal of the intraday real-time control stage is to characterize the working conditions of each furnace as accurately as possible according to the real-time demand of the primary frequency regulation of the power grid in each dispatching period, and then coordinate the output of the fused magnesium furnace group to respond to the primary frequency regulation demand of the power grid according to the winning bid capacity. Since the frequency fluctuations of the power system are random, and the adjustment capacity of the fused magnesium furnace group is also constantly changing, different coordinated control strategies for the group of furnaces will lead to significant differences in the economic cost of primary frequency regulation, thereby affecting the overall economy of the fused magnesium furnace group participating in the primary frequency regulation of the power grid. Compared with the high randomness of the frequency changes of the power grid, the dynamic changes of the working conditions of the fused magnesium furnace can be predicted in the short term. The present invention matches the real-time collected power grid frequency signal by predictive control of the future state of the fused magnesium furnace group. According to the changes in the power grid frequency, combined with the frequency modulation coefficient K of the current adjustment period t , considering the economic cost of adjustment, dynamic decision-making is made on the real-time adjustment power of the fused magnesium furnace group to achieve the optimal overall economy.

[0072] A method for intraday coordinated control of a group of fused magnesium furnaces participating in primary frequency modulation comprises the following steps:

[0073] Step 1: Design the control structure for the fused magnesium furnace group to participate in the primary frequency modulation. During the intraday real-time adjustment stage, the production control center needs to quickly control the power changes of the fused magnesium furnace group so that the fused magnesium furnace group can perform primary frequency modulation following the frequency difference changes of the power grid.

[0074] Taking into account the operability, timeliness and controllability of the control architecture, combined with the characteristics of the electric molten magnesium furnace group, which is small in scale, has a large smelting power of a single furnace, and can only adjust the smelting power under the main smelting condition, the present invention proposes a centralized control architecture, which directly exchanges information between each electric molten magnesium furnace and the control center through the production communication network. The production control center gathers and integrates smelting data, comprehensively perceives the status of the group of furnaces, and optimizes the decision-making and coordinated control of the group of furnaces from an overall perspective. The centralized control architecture is convenient for coordinating the adjustment of the smelting power of each electric molten magnesium furnace because it can integrate all the characteristic information of each electric molten magnesium furnace.

[0075] Under the centralized control architecture, the control process of the fused magnesium furnace group participating in the primary frequency modulation is divided into different adjustment periods according to whether the grid frequency crosses the dead zone. When the grid frequency does not exceed the dead zone (frequency is stable), the fused magnesium furnace group produces normally and does not adjust the operating power; after the grid frequency exceeds the dead zone, the fused magnesium furnace group increases the operating power and uses more electricity to reduce the grid frequency; when the grid frequency exceeds the dead zone (crossing the upper limit of the dead zone is called frequency upward deviation, and crossing the lower limit of the dead zone is called frequency downward deviation), the fused magnesium furnace group should be based on the frequency modulation coefficient K of the fused magnesium furnace group in the previous period. t , linearly responds to the real-time frequency deviation of the power grid to provide primary frequency regulation capacity P t PFR , until the grid frequency returns to the dead zone range. The centralized control architecture is divided into the control layer and the equipment layer according to the coverage of the energy flow and information flow of the control project. The details are as follows:

[0076] The control layer is used to determine the winning capacity P of the fused magnesium furnace group in the current period PFR , the capacity corresponds to the grid frequency deviation exceeding the frequency deviation allowable value Δf th The primary frequency modulation capacity that the fused magnesium furnace group should provide at this time is the frequency modulation coefficient K of the current period. t :

[0077]

[0078] Where Δf db Take ±0.033Hz; Δf th Take ±0.2Hz.

[0079] The production control center collects the grid frequency change information Δf in real time. If the grid frequency crosses the primary frequency regulation dead zone at this time, the production control center calculates the primary frequency regulation demand P of the current grid for the fused magnesium furnace group based on the power-frequency characteristics of the fused magnesium furnace. t PFR :

[0080]

[0081] At the same time, the adjustable capacity ΔP of the fused magnesium furnace at the current moment is evaluated through equipment layer perception t ad , combined with the grid primary frequency regulation demand P t PFR , real-time rolling optimization of the actual primary frequency modulation power ΔP of the fused magnesium furnace t act :

[0082]

[0083] The group furnace controller monitors the smelting status of each fused magnesium furnace and the adjustable power in real time Based on this, and combined with the adjustment status of each electric magnesium furnace in the previous period, the controller will optimize the primary frequency modulation power of each electric magnesium furnace and send it down. The electric magnesium furnace is connected to the public power grid through the PCC to assist the system in suppressing the grid frequency fluctuation caused by the imbalance between supply and demand power.

[0084] Step 2: Predict the operating conditions of the molten magnesium furnace. The molten magnesium furnace is a typical strongly coupled, time-varying, and strongly nonlinear system. Its full parameter description cannot be obtained by mathematical analysis. The present invention uses the method of time series prediction to predict the state changes of the molten magnesium furnace in the future based on its production status in the past period of time, providing a basis for rolling optimization decisions. The autoregressive moving average model is used to predict the adjustment capacity of the molten magnesium furnace group over a period of time. First, the input data needs to be tested for data stationarity using the ADF test, and non-stationary data needs to be differentially processed; then, the model is identified and ordered based on the tailing characteristics of the autocorrelation function graph and the truncation characteristics of the partial autocorrelation function graph in the ADF test results to determine the specific prediction model; finally, the infer() function in MATLAB is used to calculate the difference between the ARMA model predicted data and the actual data, that is, the residual test.

[0085] Based on the above prediction method, the operation status of 30 fused magnesium furnaces is predicted. The data used is the operation data of 30 fused magnesium furnaces from 21:00 on a certain day to 07:30 on the next day of a fused magnesium enterprise. The ADF test is used to test the data stability. The test results are as follows: Figure 6 shown.

[0086] from Figure 6It can be seen that the autocorrelation coefficient of the molten magnesium furnace smelting condition data shows a downward trend with the increase of the order, and the ACF is tailing, indicating that the molten magnesium furnace condition time series has long-term dependence, that is, a high-order AR term is needed to capture this dependence. It can be seen from the figure that the PACF of the molten magnesium furnace smelting condition data is truncated, and its partial autocorrelation is "0" when the lag number is 9. The specific evaluation criteria are shown in Table 1.

[0087] According to the evaluation criteria in Table 1, combined with the tailing characteristics of the autocorrelation function graph and the truncation characteristics of the partial autocorrelation function graph, the prediction model AR(9) was finally selected. The prediction model is described as follows:

[0088]

[0089] In the formula, Ψ t Indicates the predicted regulation capacity of the fused magnesium furnace; ν t represents white noise; represents the autoregressive parameter.

[0090] Through the infer() function in MATLAB, the difference between the ARMA model predicted data and the actual data is calculated, that is, the residual test. The data distribution is as follows Figure 7 shown.

[0091] It can be found that the calculated residual is a section of non-autocorrelated white noise, and the distribution of the residual data is close to the normal distribution, indicating that the established AR (9) prediction model meets the data requirements. The AR (9) model is used to predict the smelting state of the electric fused magnesium furnace. The prediction results of the smelting state of each electric fused magnesium furnace are as follows: Figure 8 shown.

[0092] The above prediction method can be used to predict the working state of each fused magnesium furnace at a certain moment. Since the fused magnesium furnace can only adjust the smelting condition under the main smelting condition, in order to reduce the decision variables of the real-time coordinated control optimization solution, a control strategy for dynamic grouping of fused magnesium furnaces is proposed. According to the prediction results, the fused magnesium furnaces (fused magnesium furnaces in the main smelting condition) that can participate in the adjustment in each control period are selected to participate in the subsequent primary frequency modulation power optimization.

[0093] Since the fused magnesium furnace can only adjust the smelting conditions under the main melting condition, in order to reduce the decision variables of the real-time coordinated control optimization solution, a control strategy for dynamic grouping of fused magnesium furnaces is proposed. According to the different operating states of the fused magnesium furnace, it is divided into the main melting cluster, the feeding cluster and the exhaust cluster, and dynamically adjusted according to the smelting process. In the real-time control stage, the fused magnesium furnaces are dynamically grouped, for example: the grouping of fused magnesium furnaces during the production period of 22:30:00-22:30:10 is shown in Table 2.

[0094] Table 2 Grouping of fused magnesium furnaces

[0095]

[0096] From the data in the table, it can be seen that the number of fused magnesium furnaces in the main melting condition is maintained at more than 20 most of the time, which provides a guarantee for the fused magnesium furnace group to participate in the primary frequency regulation of the power grid in real time. However, at some times, when multiple furnaces are in the same feeding or exhausting condition, the adjustable capacity of the group furnace is low, and it is even unable to meet the real-time primary frequency regulation requirements. At this time, the rolling optimization strategy provides a decision-making basis for it to ensure the overall economy after participating in the primary frequency regulation.

[0097] Step 3: Rolling optimization of the primary frequency modulation power of the fused magnesium furnace group. Intraday coordinated control requires real-time control, and does not require direct global optimization control, but considers optimizing decisions through the current local time period. Therefore, the present invention continuously rolls the limited optimization prediction time domain and realizes global optimization on the basis of local optimization. The optimization result is N u The control sequence within the time period, but in actual application, only the first control result is executed, and the above process is repeated at the next sampling moment. At each sampling moment, the current state of the system is used as the initial condition for optimization. This strategy makes each rolling optimization more in line with the frequency stability requirements of the current system and the actual working status of each molten magnesium furnace, thereby improving the robustness and effectiveness of the molten magnesium furnace group participating in the primary frequency regulation of the power grid. Since this continuous rolling optimization process itself can adapt to the changes in the state of the molten magnesium furnace group, there is no need to set up a feedback correction link.

[0098] The prediction time domain is N p The control time domain consists of N time periods. u The period composition (N u <N p ). In this optimization framework, using N p The prediction results of each period are used to characterize the future behavior of the fused magnesium furnace group, and the next N u Control target for each period of time.

[0099] The key to whether the overall economic improvement and frequency regulation requirements can be taken into account during the real-time adjustment stage lies in the design of the economic cost function. Based on the idea of ​​rolling optimization, the present invention proposes an economic optimization control model that takes into account the adjustment characteristics of the group of fused magnesium furnaces. The goal is to minimize the economic cost after the group of furnaces participates in the primary frequency regulation, and consider the achievement of the primary frequency regulation target of the group of fused magnesium furnaces. The compensation income for the primary frequency regulation of fused magnesium is determined by the primary frequency regulation capacity that won the bid the day before. Therefore, the consideration of the economic efficiency within the day is mainly to minimize the economic cost. The frequency regulation deficiency penalty, production loss, power over-limit penalty and equipment loss of the group of furnaces reflect the economic cost of the real-time adjustment stage. The effect of the group of furnaces adjusting the power in real time to track the frequency difference signal reflects its primary frequency regulation performance. Therefore, the economic optimization control model is designed as follows:

[0100]

[0101] Where P min Generally P max Generally

[0102] The penalty Y1 for insufficient frequency modulation in the control time domain is:

[0103]

[0104] In the formula, ε represents the penalty unit price for insufficient frequency regulation, which is 5 yuan / kW·h.

[0105] If the primary frequency modulation power of the fused magnesium furnace exceeds the adjustable range allowed by the fused magnesium furnace at that moment, it will affect the product quality. In the real-time adjustment stage during the day, in order to participate more in the primary frequency modulation and improve the overall economy, some periods will exceed the adjustable range allowed by the fused magnesium furnace at that moment, resulting in certain production losses, that is, the production loss Y2 in the control time domain:

[0106]

[0107] Where, κ represents the unit price of production loss of electric fused magnesium furnace, which is 20 yuan / MW.

[0108] Control the power limit penalty Y3 in the time domain:

[0109]

[0110] Control the equipment loss Y4 in the time domain:

[0111]

[0112] In the formula, φ represents the unit price of loss each time the equipment is adjusted, which is 0.2 yuan per time.

[0113] In the model, except for the start time, other (N u -1) The working state of the fused magnesium furnace at each decision moment needs to be based on the predicted working state value plus the frequency adjustment amount decided from the predicted moment to the moment before that moment, so that the influence of the decision result of the previous moment on the decision of the subsequent moment can be considered in the entire optimization period. According to the above model, N u Each fused magnesium furnace has a frequency modulation power control sequence during the time period, and forms N u The actual primary frequency modulation total power control sequence of the group furnaces in the time period. Under the centralized control structure, the control center sends the first control result of the primary frequency modulation power adjustment of each fused magnesium furnace to each fused magnesium furnace. Repeat the above process at the next sampling time.

[0114] In order to verify the control effect of the economic optimization control model considering the adjustment cost proposed in the present invention, it is set as control method 1. Another control model with the goal of tracking the minimum error of the frequency difference signal and not considering the adjustment cost is set as control method 2. The frequency data of a provincial network from 21:00 on a certain day to 07:30 on the next day is used to verify the real-time frequency adjustment effect. The sampling time constant t in the example is d is 1 second, controlling the time domain N u is 10, prediction time domain N p is 15. The result is Fig. 9 shown.

[0115] The benchmark frequency regulation capacity in the figure is the primary frequency regulation demand of the current power grid for the fused magnesium furnace group P t PFR .from Fig. 9 It can be seen that both methods can effectively track the primary frequency modulation signal. In comparison, the tracking effect of the control method considering economic cost is slightly inferior to that of the control method not considering economic cost. This is because the control method not considering economic cost takes the minimum error of tracking the primary frequency modulation signal as the objective function, while the control method considering economic cost takes economic efficiency into consideration and sacrifices the performance of primary frequency modulation tracking in some periods to obtain higher overall benefits. Specifically:

[0116] In the figure, the grid frequency in area ① is higher than the upper limit of the dead zone, and the fused magnesium furnace needs to lower the electrode to increase the capacity power to respond to the grid's primary frequency regulation demand. However, it is considered that too large an adjustment range is not good for equipment safety, so the adjustment range should be appropriately reduced to ensure safe production. After local amplification, it can be seen that although the response accuracy and action rate of the fused magnesium furnace under the control strategy considering economic costs are not as good as the control method without considering economic costs, its performance indicators still meet the grid requirements.

[0117] It can be seen from area ② in the figure that the grid frequency fluctuates at 184-195 seconds, and the electric fused magnesium furnace group has good adjustment ability. The control method considering economic cost coordinates the group of furnaces to respond to the changes in grid frequency in a timely and accurate manner. Under the premise of a certain frequency regulation mileage, the overall economy is ensured by ensuring the frequency regulation performance of the electric fused magnesium furnace.

[0118] In the dashed box of area ③ in the figure, the primary frequency modulation signal fluctuates violently. Under the control method without considering the economic cost, the fused magnesium group furnace responds quickly and controls the smelting power of the group furnace to drop rapidly to ensure the accuracy of the response time and action rate. Under the control strategy considering the economic cost, the economic cost is weighed and adjusted, and some frequency modulation performance is sacrificed. The group furnace is controlled to respond to the primary frequency modulation demand of the power grid within the range allowed by the dispatching agency.

[0119] The grid frequency at 600-900 seconds exceeds the upper limit of the dead zone for a long time, as shown in area ④ in the figure. Due to the constraints set for the number of adjustments and the continuous adjustment time, the grid frequency regulation requirements cannot be met at some times to ensure equipment safety, which will cause the company to receive insufficient frequency regulation penalties. After local zooming in, it can be clearly observed that as the adjustment capacity of some electric magnesium furnaces is restored, the control method considering economic costs can quickly control their response to frequency regulation requirements, minimize unnecessary penalties, and improve overall economic efficiency.

[0120] In order to clarify the economic efficiency of the control method considering economic cost after sacrificing part of the adjustment performance, the penalty situation under the two control strategies during the adjustment period is statistically analyzed, see Table 3.

[0121] Table 3 Comparison of economic performance of different control strategies

[0122]

[0123] The adoption of a control strategy that takes economic costs into consideration during the current adjustment period has reduced the enterprise's frequency adjustment cost by 229 yuan, and the overall economic efficiency has been improved. However, the profit and loss situation of some periods cannot prove the overall economic efficiency of the entire production cycle. In order to prove the effectiveness of the proposed control strategy that takes economic costs into consideration, an overall economic efficiency verification was designed.

[0124] The frequency data of a provincial network from 21:00 to 07:30 the next day was selected for overall economic verification. The economic optimization control model considering the adjustment cost is set as control method 1, with the goal of tracking the minimum frequency difference signal error, and the control model without considering the adjustment cost is control method 2. The adjustment results are as follows Fig.10 and Fig.11 shown.

[0125] contrast Fig.10 and Fig.11 It can be found that the grid frequency at time t1 is -0.22Hz, which has exceeded the lower limit of the grid frequency deviation of -0.2Hz. According to formula (2), the fused magnesium furnace group should adjust the smelting power according to the winning capacity, that is, reduce it by 4.74MW. However, the fused magnesium furnace group cannot provide the corresponding adjustment power at this time. The current optimal adjustment capacity is 3.92MW obtained by the economic optimization control model considering the economic cost. Although the fused magnesium furnace group will be punished by a 0.82MW frequency regulation deficiency at this time, compared with providing a 4.74MW adjustment power without considering the economic cost, the overall economic efficiency of the fused magnesium enterprise is improved, and the adjustment cost is reduced by 496 yuan.

[0126] At t2, the grid frequency is 0.21Hz, which exceeds the upper limit of the grid frequency deviation of 0.2Hz. According to formula (2), the fused magnesium furnace group should adjust the smelting power according to the bid capacity, that is, reduce it by 5.09MW. The actual primary frequency modulation power of the fused magnesium furnace group is 3.86MW. Although the fused magnesium furnace group will be penalized by 1.23MW of insufficient primary frequency modulation at this time, if the adjustment cost is not considered and 5.09MW of primary frequency modulation capacity is directly provided, the fused magnesium enterprise will face a large maximum demand over-limit penalty, and the total adjustment cost will reach 2565 yuan. Considering the adjustment cost, the control strategy chooses to reduce the primary frequency modulation capacity to avoid the maximum demand over-limit penalty and ensure the overall economy.

[0127] During the period of time t3, the grid frequency has long crossed the primary frequency regulation dead zone, and the fused magnesium furnace group continues to adjust its own smelting power. Due to the limitation of the number of single furnace adjustments, the number of adjustable fused magnesium furnaces in some moments during this period has decreased, so the primary frequency regulation capacity has been insufficient for many times. At t3, the grid frequency difference is -0.12Hz. From the current frequency regulation coefficient, it can be seen that the fused magnesium furnace group needs to reduce the smelting power by 3.42MW. Limited by the setting of the number of adjustments, the optimization results show that the fused magnesium furnace in an adjustable state provides 2.93MW smelting power to optimize the overall economy, but will face a frequency regulation cost of 906 yuan. If the 3.42MW smelting power is forcibly adjusted at this time, it will lead to certain production losses, and the adjustment cost will increase to 1463 yuan. Therefore, the fused magnesium furnace group chooses to reduce the primary frequency regulation capacity to reduce the economic cost and improve the overall economy.

[0128] The total adjustment cost during all optimization periods (21:45-07:30 the next day) is calculated to be 36,116 yuan under the adjustment control method that takes economic cost into consideration and 44,304 yuan under the adjustment control method that does not take economic cost into consideration.

[0129] In all optimized periods, the total primary frequency regulation demand is 11.38MW·h according to the frequency difference signal and the primary frequency regulation capacity that was won in the previous day. Under the regulation control mode that takes economic cost into consideration, the total primary frequency regulation shortage is 0.09MW·h, accounting for 0.80% of the total primary frequency regulation demand.

[0130] It can be seen that the regulation and control method that takes economic costs into consideration can reduce the regulation costs and reduce the economic losses of enterprises under the premise of losing very little primary frequency regulation power, which is beneficial to improving the overall economic efficiency of electric fused magnesium enterprises participating in primary frequency regulation.

[0131] In summary, for the intraday coordinated control of the electric fused magnesium furnace group participating in the primary frequency modulation, the present invention considers the economy of the electric fused magnesium furnace group in the frequency modulation process, takes the minimum economic cost as the optimization goal, takes into account the primary frequency modulation performance of the electric fused magnesium furnace group, establishes an economic optimization control model, and coordinates the electric fused magnesium furnace group to participate in the primary frequency modulation of the power grid in real time. The dynamic change process of the working condition of the electric fused magnesium furnace is predicted in the short term by means of time series prediction. Based on the working condition prediction of the electric fused magnesium furnace and combined with the real-time collected power grid frequency information, the real-time optimal primary frequency modulation capacity of the electric fused magnesium furnace group is solved in a rolling manner under the condition of meeting relevant constraints and control objectives. The example shows that the control method considering the regulation cost proposed in the present invention can effectively realize the intraday coordinated control of the electric fused magnesium furnace group participating in the primary frequency modulation of the power grid, improve the overall economy while ensuring the frequency modulation quality of the primary frequency modulation.

[0132] The above-described embodiments merely express the implementation methods of the present invention, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0133] If the fused magnesium furnace group does not meet the primary frequency regulation demand P of the current power grid for the fused magnesium furnace group before the frequency is restored to the dead zone, t PFR The power dispatching agency will impose insufficient frequency regulation penalties on the molten magnesium enterprises based on the difference between the primary frequency regulation demand and the actual primary frequency regulation power of the molten magnesium furnace group.

[0134] The present invention is based on the working condition prediction of the molten magnesium furnace and combines the real-time collected power grid frequency information to solve the optimal primary frequency modulation power of each molten magnesium furnace at the current moment under the condition of meeting relevant constraints and control objectives, and continuously optimizes the capacity of the molten magnesium furnace group participating in the primary frequency modulation as the adjustment proceeds.

Claims

1. A method for intraday coordinated control of a group of fused magnesium furnaces participating in primary frequency modulation, characterized in that: The following steps are involved: Step 1, using a centralized control architecture as the control architecture for real-time coordination and regulation of a group of fused magnesium furnaces; Step 2: predict the operating conditions of the fused magnesium furnace, and use a control strategy to coordinate the power adjustment of each fused magnesium furnace; use a time series prediction method to predict the state change of the fused magnesium furnace in the future based on the production state of the fused magnesium furnace in the past period of time; Step 3, propose a control strategy for dynamic grouping of electric molten magnesium furnaces; based on the operating condition prediction of the electric molten magnesium furnace and combined with the real-time collected power grid frequency information, construct an economic optimization control model that takes into account the regulation cost, and solve the optimal primary frequency regulation power of each electric molten magnesium furnace at the current moment under the condition of meeting the relevant constraints and control objectives, and continuously optimize the capacity of the electric molten magnesium furnace group participating in the primary frequency regulation as the regulation proceeds.

2. The intraday coordinated control method for a group of fused magnesium furnaces participating in primary frequency modulation according to claim 1 is characterized in that: The following steps are involved: Step 1: Specifically: A centralized control architecture is adopted to directly exchange information between each fused magnesium furnace and the control center through the production communication network; the production control center aggregates and integrates smelting data, comprehensively perceives the status of the group of furnaces, and makes optimized decisions and coordinated control of the group of furnaces; under the centralized control architecture, the control process of the fused magnesium group furnaces participating in the primary frequency modulation is divided into different adjustment periods according to whether the grid frequency crosses the dead zone: When the grid frequency does not exceed the dead zone, that is, when the grid frequency is stable, the fused magnesium furnace group will produce normally without adjusting the operating power; When the grid frequency exceeds the dead zone, the definition of crossing the upper limit of the dead zone is called upward frequency deviation, and the fused magnesium furnace group increases the operating power and uses more electricity to reduce the grid frequency; the definition of crossing the lower limit of the dead zone is called downward frequency deviation, and the fused magnesium furnace group reduces the operating power and uses less electricity to increase the grid frequency; the fused magnesium furnace group is based on the frequency modulation coefficient K of the fused magnesium furnace group in the previous period. t , linearly responds to the real-time frequency deviation of the power grid to provide primary frequency regulation capacity P t PFR , until the grid frequency returns to within the dead zone; The centralized control architecture is divided into a control layer and a device layer according to the coverage of the energy flow and information flow of the control project; Step 2: Specifically: The autoregressive moving average ARMA model is used to predict the adjustment capacity of the fused magnesium furnace group within a period of time; First, the input data is tested for data stationarity using the extended Dick Fuller ADF test, and non-stationary data needs to be processed by difference; Then, combined with the tailing characteristics of the ACF graph and the truncation characteristics of the PACF graph in the ADF test results, the model is identified and ordered according to the evaluation criteria to determine the specific prediction model. The prediction model is described as: In the formula, Ψ t Indicates the predicted adjustment capacity of the fused magnesium furnace; p and q represent the autoregressive order and moving average order of the prediction model respectively; where α a , β a They represent the undetermined coefficients that are not "0"; t , χ t-b represents an independent error term; X t-a represents a stationary, normal time series; Finally, MATLAB is used to calculate the difference between the ARMA model predicted data and the actual data, i.e., the residual test; The control strategy of dynamic grouping of fused magnesium furnaces is adopted. According to the different operating states of the fused magnesium furnaces, they are divided into main melting cluster, charging cluster and exhaust cluster, and dynamically adjusted according to the smelting process; the ARMA model is used to predict the working state of each fused magnesium furnace at a certain time, and the fused magnesium furnaces that can participate in the adjustment in each control period are selected according to the prediction results to participate in the subsequent primary frequency modulation power optimization; Step 3: Specifically: Rolling optimization of the primary frequency modulation power of the fused magnesium furnace; rolling the limited optimization prediction time domain continuously, and realizing global optimization on the basis of local optimization; the optimization result is N u In the control sequence within a time period, in actual application, only the first control result is executed, and the above process is repeated at the next sampling time; at each sampling time, the current state of the system is used as the initial condition for optimization; The prediction time domain is N p The control time domain consists of N time periods. u The period consists of N u <N p ; In this optimization framework, using N p The prediction results of each period are used to characterize the future behavior of the fused magnesium furnace group, and the next N u Control target for each period; Based on the idea of ​​rolling optimization, an economic optimization control model considering the regulation characteristics of fused magnesium furnaces is proposed. The goal is to minimize the economic cost of the furnaces after the furnaces participate in the primary frequency regulation, and the achievement of the primary frequency regulation target of the fused magnesium furnaces is considered. The economic optimization control model is designed as follows: Where, Y represents the economic cost of the real-time adjustment stage; Y1 represents the penalty for insufficient frequency regulation in the control time domain; Y2 represents the production loss in the control time domain; Y3 represents the penalty for power over-limit in the control time domain; Y4 represents the equipment loss in the control time domain; J represents the number of fused magnesium furnaces in the enterprise; T represents the scheduling period; P j,t It represents the operating power of the jth fused magnesium furnace in the fused magnesium furnace group at time t; represents the rated power of the jth fused magnesium furnace in the fused magnesium furnace group at time t; Ω represents the maximum number of adjustments of a single furnace within 15 minutes; They represent the 0-1 variables of the power adjustment of the fused magnesium furnace in the τth adjustment stage, Indicates that the current stage is upward. Indicates an upward adjustment at the current stage; They represent the 0-1 variables of the power adjustment of the fused magnesium furnace in the τth and τ-1th adjustment stages, respectively. Indicates the current stage of adjustment. Indicates that there is no adjustment in the current stage; N(t) indicates the number of fused magnesium furnaces that can participate in the adjustment in the tth period; They represent the power increase and decrease of the jth fused magnesium furnace in the fused magnesium furnace group at time t; P min , P max Respectively represent the minimum and maximum power of the electric magnesium furnace, P min for P max for In the economic optimization control model, except for the starting time, other (N u -1) The working state of the fused magnesium furnace at each decision moment needs to be based on the predicted working state value plus the frequency adjustment amount decided from the predicted moment to the moment, and then the influence of the decision result of the previous moment on the decision of the subsequent moment is considered in the entire optimization period; according to the economic optimization control model, solve N u Each fused magnesium furnace has a frequency modulation power control sequence during the time period, and forms N u The actual primary frequency modulation total power control sequence of the group of furnaces in the time period; under the centralized control structure, the control center sends the first control result of the primary frequency modulation power adjustment of each electric magnesium furnace to each electric magnesium furnace; and repeats the above process at the next sampling time.

3. The intraday coordinated control method for a group of fused magnesium furnaces participating in primary frequency modulation according to claim 2 is characterized in that: In step 1, the centralized control architecture is divided into a control layer and a device layer according to the coverage of the energy flow and information flow of the control project, as follows: The control layer is used to determine the winning capacity P of the fused magnesium furnace group in the current period PFR , the capacity corresponds to the grid frequency deviation exceeding the frequency deviation allowable value Δf th The primary frequency modulation capacity that the fused magnesium furnace group should provide at this time is the frequency modulation coefficient K of the current period. t : Where Δf db Indicates the primary frequency modulation dead zone; The production control center collects the grid frequency change information Δf in real time. If the grid frequency crosses the primary frequency regulation dead zone at this time, the production control center calculates the primary frequency regulation demand P of the current grid for the fused magnesium furnace group based on the power-frequency characteristics of the fused magnesium furnace. t PFR : Where Δf t Indicates the real-time frequency deviation of the power grid; It means that the grid frequency deviation exceeds the frequency regulation dead zone but is not higher than the allowable value of frequency deviation; Indicates that the grid frequency deviation is higher than the allowable value of frequency deviation; At the same time, the adjustable capacity ΔP of the fused magnesium furnace at the current moment is evaluated through equipment layer perception t ad , combined with the grid primary frequency regulation demand P t PFR , real-time rolling optimization of the actual primary frequency modulation power ΔP of the fused magnesium furnace t act : In the formula, represents the actual primary frequency modulation power of the jth fused magnesium furnace in the fused magnesium furnace group at time t, which is the decision variable for intraday economic optimization and regulation; n is the number of fused magnesium furnaces involved in the adjustment; The group furnace controller monitors the smelting status and adjustable power of each fused magnesium furnace in real time Based on this, and combined with the adjustment status of each electric magnesium furnace in the previous period, the controller will optimize the primary frequency modulation power of each electric magnesium furnace and connect the electric magnesium furnace to the public power grid through a public connection point to assist the system in suppressing the grid frequency fluctuations caused by the imbalance between supply and demand power.

4. The intraday coordinated control method for a group of fused magnesium furnaces participating in primary frequency modulation according to claim 2 is characterized in that: The evaluation criteria in step 2 are shown in Table 1; Table 1 Evaluation criteria 。 5. The intraday coordinated control method for a group of fused magnesium furnaces participating in primary frequency modulation according to claim 2, characterized in that: In step 3: The penalty Y1 for insufficient frequency modulation in the control time domain is: Where Δf represents the real-time frequency deviation; ε represents the penalty unit price for insufficient frequency modulation; X Δf＞0.033Hz Indicates that the grid frequency difference exceeds the upper limit of the frequency regulation dead zone; X Δf＜-0.033Hz Indicates that the grid frequency difference exceeds the lower limit of the frequency regulation dead zone; The production loss Y2 in the control time domain is: In the formula, represents the indicator function, When it is true, the function value is 1, otherwise it is 0; κ represents the unit price of production loss of electric fused magnesium furnace; represents the indicator function, The function value is 1 when it is true, otherwise it is 0; The power limit penalty Y3 in the control time domain is: Where P M represents the maximum demand contract value; ω represents the maximum demand electricity price; The equipment loss Y4 in the control time domain is: In the formula, 3 represents the three-phase electrode; s t It represents the number of adjustments in the tth period; φ represents the unit price of loss each time the equipment is adjusted.

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